Issue #189

February 5, 2023

This week’s Data Science Book is " Practical Linear Algebra for Data Science " by M. X. Cohen, a book written for self-studying learners who need to learn how to apply linear algebra in their work. The book is self-contained and can be used as a standalone resource, but it can also be used as a supplement to a lecture-based course. Whether you are trying to enhance your understanding of linear algebra or learn the subject from scratch, this is a valuable resource that provides a clear and practical approach to the subject. The author is an excellent instructor that recognizes that traditional linear algebra textbooks can be frustrating for those looking to use the subject as a tool for understanding data, statistics, deep learning, image processing, and other technical fields. Instead of memorizing equations and abstract proofs, the author provides clear explanations and practical examples to help the reader understand how to think about matrices, vectors, and operations. The focus of the book is to help the reader develop a visual, geometric, intuition for linear algebra and how to implement these concepts in Python code, particularly for applications in machine learning and data science.

Practical Linear Algebra for Data Science

1. Vectorization: say goodbye to loops in Python [medium.com/@matteo.bernard]
2. Finally, a Fast Algorithm for Shortest Paths on Negative Graphs [quantamagazine.org]
3. New AI classifier for indicating AI-written text [openai.com]
4. Some remarks on Large Language Models [gist.github.com/yoavg]
5. Building Effective Ratio Features for Machine Learning Models [soliduslabs.com]
6. John Carmack's 'Different Path' to Artificial General Intelligence [dallasinnovates.com]
7. How to build a scraping tool for Linkedin in 7 minutes [blog.devgenius.io]

• The Technology of Decentralized Finance (DeFi) (R. Auer, B. Haslhofer, S. Kitzler, P. Saggese, F. Victor)
• Activity networks determine project performance (A. Vazquez, I. Pozzana, G. Kalogridis, C. Ellinas)
• Learning from data with structured missingness (R. Mitra, S. F. McGough, T. Chakraborti, C. Holmes, R. Copping, N. Hagenbuch, S. Biedermann, J. Noonan et al)
• ChatGPT: five priorities for research (E. A. M. van Dis, J. Bollen, W. Zuidema, R. van Rooij, C. L. Bockting)
• Infrastructure adaptation and emergence of loops in network routing with time-dependent loads (A. Lonardi, E. Facca, M. Putti, C. De Bacco)
• Exact and rapid linear clustering of networks with dynamic programming (A. Patania, A. Allard, J.-G. Young)
• Impact of the Euro 2020 championship on the spread of COVID-19 (J. Dehning, S. B. Mohr, S. Contreras, P. Dönges, E. N. Iftekhar, O. Schulz, P. Bechtle, V. Priesemann)
• Higher-Order Patterns Reveal Causal Timescales of Complex Systems (L. V. Petrović, A. Wegner, I. Scholtes)

Python for linear algebra (for absolute beginners)

February 5, 2023

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